Spss v 26 for windows

Manufactured by IBM

Sourced in United States

SPSS v.26 for Windows is a software package used for statistical analysis. It provides a range of tools for data management, analysis, and visualization. The software is designed to help users understand data, identify trends, and make informed decisions. SPSS v.26 is compatible with the Windows operating system.

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Prism v 8 for windows, by GraphPad (1 mentions) Spss statistic, by IBM (1 mentions) Prism v 7 for windows, by GraphPad (1 mentions)

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SPSS software v. 26.0 for Windows SPSS for Windows version v.26 SPSS Statistics V.26.0 for Windows SPSS statistical package for Windows v.26 SPSS Statistics for Windows v.26.0 SPSS 26 for Windows SPSS for Window SPSS 26.0 SPSS Windows SPSS vs.

63 protocols using spss v 26 for windows

Non-parametric Analysis of Small Samples

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Due to the small sample of patients, a non-parametric approach was used. Data were expressed as median (IQR) or percentage (%). Differences between the two groups were analyzed using the Mann–Whitney U test. Correlation analysis was performed using Spearman’s r.
Statistical significance was set at p <0.05. The statistical analysis was performed using SPSS for Windows V.26.0 (SPSS Inc., Chicago, IL, USA).

Antoniotti V., Bellone S., Gonçalves Correia F.P., Peri C., Tini S., Ricotti R., Mancioppi V., Gagliardi M., Spadaccini D., Caputo M., Corazzari M, & Prodam F. (2022). Calreticulin and PDIA3, two markers of endoplasmic reticulum stress, are associated with metabolic alterations and insulin resistance in pediatric obesity: A pilot study. Frontiers in Endocrinology, 13, 1003919.

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Evaluating clinical handover skills

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Data analysis was performed using SPSS for Windows v26.0. Descriptive statistics were used to calculate the mean scores and standard deviations for each scale. The baseline characteristics and research outcomes were compared between the groups using independent samples t-test for parametric variables, chi-squared and Fisher’s exact tests for categorical variables and Mann-Whitney U test for continuous variables. The Pearson correlation test and Spearman’s correlation were used to analyse the inter-variable correlations. An analysis of covariance (ANCOVA) was used to examine the between-subjects effects on self-efficacy and communication skill competence in conducting clinical handovers after controlling for the significantly correlated variables. A paired sample t-test was used to determine the within-subjects effects on self-efficacy.

Chung J.Y., Li W.H., Cheung A.T., Ho L.L, & Chung J.O. (2022). Efficacy of a blended learning programme in enhancing the communication skill competence and self-efficacy of nursing students in conducting clinical handovers: a randomised controlled trial. BMC Medical Education, 22, 275.

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Analysis of Intraocular Pressure Fluctuations

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Definitions of the terms used to describe fluctuation are shown below: (1) Peak IOP: highest IOP recorded in the stated time period; (2) Trough IOP: lowest IOP recorded in the stated time period; (3) IOP fluctuation: Peak IOP minus trough measured in the stated time period and (4) Mean amplitude of IOP excursions (MAPE): MAPE was calculated as the arithmetic mean value of the relevant IOP fluctuations meeting this criterion.19 (link) All categorical data were represented by frequency with percentage and it was analysed by χ², Fisher’s exact test. Continuous data were presented by mean with SD and tested by Student’s t-test. Pearson correlation analysis and multivariate regression analysis were used to analyse the association with IOP. All p values were two sided and were considered statistically significant when p<0.05. Statistical analysis was carried out using a commercially available statistical software package (SPSS for Windows, V.26.0).

Yang Y., Zhang X., Chen Z., Wei Y., Ye Q., Fan Y., Nathwani N., Gazzard G, & Yu M. (2022). Intraocular pressure and diurnal fluctuation of open-angle glaucoma and ocular hypertension: a baseline report from the LiGHT China trial cohort. The British Journal of Ophthalmology, 107(6), 823-827.

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Factors Influencing Everyday Activity Levels

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The population was described and comparisons between those who identified and those who did not identify reduced/ceased activities were conducted using t tests for continuous variables, χ² or Kruskal-Wallis tests for categorical variables. Logistic regression was used to explore the impact of known confounders on the model exploring the factors likely to influence participation levels in everyday activities. Statistical analysis was conducted using SPSS for Windows V.26.0. (SPSS Chicago, Illinois, 2011).

Kochovska S., Currow D., Chang S., Johnson M., Ferreira D., Morgan D., Olsson M, & Ekström M. (2022). Persisting breathlessness and activities reduced or ceased: a population study in older men. BMJ Open Respiratory Research, 9(1), e001168.

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Statistical Analysis of Animal Experiments

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The Statistical Package for the Social Sciences (SPSS for Windows, v26.0; SPSS Inc., Chicago, IL, USA) was used to analyze all of the generated data. A one-way ANOVA was used to analyze data from the digestion experiment, dry matter intake, and body weight change. Morphometry parameters and immunological parameters (cell –mediated and humoral immunity) were analysed using a two-way ANOVA with repeated measure analysis. General linear model (GLM) approaches were used to assess the experimental data of parameters that were periodically collected (antioxidant concentration, fecal score, metabolites, microbiota, and short-chain fatty acids) with the fixed effects of treatments, time/period, and treatments × time/period. Pairwise comparisons of the mean values were tested by Tukey’s test at the significance level (p < 0.05). The mean and standard error mean of each set of data are shown for each parameter.

Yadav M., Kumar S., Parsana Y., Chauhan N., Tyagi N., Mondal G, & Samanta A.K. (2024). Non-encapsulated, encapsulated, and lyophilized probiotic Limosilactobacillus reuteri SW23 influenced the growth and gut health in calves. Scientific Reports, 14, 7657.

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Progression Risk Factors Analysis Protocol

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SPSS for Windows, V.26.0 (SPSS Inc IMB Company) was used for data analysis. Kolmogorov-Smirnov test of normality was performed for continuous data. Variables were presented as means with SD and medians with IQR for normally and non-normally distributed variables, respectively. Frequency (%) was given for counts. Mann-Whitney test was used to compare continuous values between groups. χ² test or Fisher’s exact test was used to compare categorical variables, such as proportions, between groups. Univariable and multivariable logistic regression analyses were used to investigate risk factors associated with progression to JoAS. ORs with 95% CIs were calculated. Variables identified in the univariate analysis (p<0.10) were entered into a forward stepwise multiple logistic regression model. P values of <0.05 were considered statistically significant.

Li H.G., Wang D.M., Shen F.C., Huang S.X., Hou Z.D., Lin L, & Xiao Z.Y. (2021). Risk factors for progression of juvenile-onset non-radiographic axial spondyloarthritis to juvenile-onset ankylosing spondylitis: a nested case–control study. RMD Open, 7(3), e001867.

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Analysis of Cardiovascular Risk Factors

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The mean ± standard deviation (SD) and median (interquartile range) were used to prescribe normally and non-normally distributed data, respectively. Student’s t-test and Chi-squared (χ²) test were conducted to compare the means and proportions of each group. A Cox proportional hazard regression was used to investigate the baseline CV risk scores, cIMT, CP, and the time to the first MACE occurrence. The effect of the presence of crystal deposition and CP at baseline on MACE-free survival was assessed using Kaplan–Meier survival analysis. The survival distributions were compared by using log-rank testing. Statistical analyses were performed with SPSS for Windows, v. 26.0 (SPSS Inc., Chicago, IL, USA). p-values of <0.05 were considered significant. p-values of <0.01 were considered highly significant.

Wang Y., Deng X., Zhang X., Geng Y., Ji L., Song Z, & Zhang Z. (2023). Presence of tophi and carotid plaque were risk factors of MACE in subclinical artherosclerosis patients with gout: a longitudinal cohort study. Frontiers in Immunology, 14, 1151782.

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Comparative Analysis of Treatment Outcomes

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Statistical analysis was carried out with SPSS for Windows (v. 26.0), using unidirectional variance analysis (ANOVA). The comparisons of means were carried out using Duncan’s test (p < 0.05).

Hernández-Apaolaza L., Escribano L., Zamarreño Á.M., García-Mina J.M., Cano C, & Carrasco-Gil S. (2020). Root Silicon Addition Induces Fe Deficiency in Cucumber Plants, but Facilitates Their Recovery After Fe Resupply. A Comparison With Si Foliar Sprays. Frontiers in Plant Science, 11, 580552.

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Workplace Outness and Associated Factors

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Descriptive analyses included means and SD for metric variables, and categorical variables are presented as frequencies and percentages. To assess differences between workers who were categorised as out at work and those who were not, we used t-test and Mann-Whitney U test, depending on data distribution with χ 2 test being used for differences in categorical variables. We performed a multivariable logistic regression model (mutually adjusted, listwise exclusion of cases) to assess which worker and workplace characteristics were associated with workplace outness. Variables in the model were chosen based on the univariate analysis using the cut-off point of p<0.2, 24 (link) with the largest group chosen as the reference in the model. Statistical analyses were done using SPSS for Windows V.26.0, all tests were two tailed, with p values less than 0.05 denoting statistical significance.

Markovic L., Schönherr D., Zandonella M., Gil-Salmeron A., Smith L., McDermott D., Yang L., Dorner T.E., Mües H, & Grabovac I. (2022). Associations between workplace characteristics and 'outness' in LGBTI workers in Austria. Occupational and environmental medicine, 79(1).

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Exploratory Factor Analysis of Digital Teaching and Learning

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Survey data were first analyzed using descriptive statistics, with continuous variables reported as mean ± standard deviation (SD) and categorical variables reported as frequency (percent). An exploratory factor analysis was conducted to identify DT teaching and learning (DT-TL) constructs using principal components analysis with varimax rotation and Kaiser rule (ie, eigenvalues < 1.0) for student survey items also included in the Liedka and Bahr study [24 ]. Bivariate correlations were calculated using Pearson rho (r_p) and reliabilities were calculated using Cronbach α. Group comparisons were examined using independent t-tests and one-way ANOVA with Bonferroni post hoc analysis. Parametric statistics were considered appropriate due to normality of data and sufficient sample size. Statistical significance was established at α<0.05. All data analysis was performed in SPSS for Windows, v26 (IBM, Armonk, NY).

McLaughlin J.E., Chen E., Lake D., Guo W., Skywark E.R., Chernik A, & Liu T. (2022). Design thinking teaching and learning in higher education: Experiences across four universities. PLoS ONE, 17(3), e0265902.

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